Robot learns manipulation tasks from simple visual examples
In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks
Robotics
Summary
Robots often need many examples and complex training to learn tasks. This work clearly defines what robots should learn from watching visual demonstrations and offers a simple system called SimpleICL that doesn't require expensive data or pretraining. The system learns to recognize actions, objects, and how to use them together, working well in both simulations and real-world tests. The authors will share all their data and tools so others can build on their work.
What this means in practice
- •For robotics engineers: Create robot controllers that learn to manipulate objects from a few visual examples without large pretraining datasets.
- •For automation integrators: Deploy adaptable robotic systems that can quickly infer new tasks from simple demonstration videos in manufacturing setups.
Authors
Minxing Li, Minghao Han, Weizhi Zhao, Hanwen Wang, Xiangshuo Liu, Shuyao Shang, Jingxiang Zhou, Mingchao Sun, Hongyu Pan, Mu Xu, Yu Liu, Lue Fan, Zhaoxiang Zhang
Abstract
We study robotic in-context learning (ICL), an emerging paradigm that enables robots to infer and execute tasks from visual demonstrations. Despite its growing promise, the problem itself remains under-defined: a visual demonstration simultaneously conveys action trajectories, object semantics, manipulation affordances, spatial relations, and task goals, making it unclear what information the robot is actually expected to follow. In this work, we first provide a clear problem definition of robot ICL that explicitly defines its learning target and resolves this fundamental prompt ambiguity. Building on this definition, we develop a minimalist and reproducible ICL framework (SimpleICL) with a visual prompt encoder and a low-cost data collection protocol. Without massive pre-training or specialized data infrastructure, our framework achieves strong performance in both simulation and real-world environments. Extensive experiments further reveal several key properties of robot ICL, including action, semantic, composition, and affordance discrimination. We will fully open-source our data and training pipeline to facilitate systematic and reproducible research on robot ICL. The project page can be found at https://simpleicl.github.io/simpleicl.